Search Results for author:
Found 21 papers, 7 papers with code
SwapAnything: Enabling Arbitrary Object Swapping in Personalized Visual Editing
Compared with existing methods for personalized subject swapping, SwapAnything has three unique advantages: (1) precise control of arbitrary objects and parts rather than the main subject, (2) more faithful preservation of context pixels, (3) better adaptation of the personalized concept to the image.
Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks
Diffusion models trained on large-scale datasets have achieved remarkable progress in image synthesis.
AnaMoDiff: 2D Analogical Motion Diffusion via Disentangled Denoising
We present AnaMoDiff, a novel diffusion-based method for 2D motion analogies that is applied to raw, unannotated videos of articulated characters.
Localizing and Editing Knowledge in Text-to-Image Generative Models
We adapt Causal Mediation Analysis for text-to-image models and trace knowledge about distinct visual attributes to various (causal) components in the (i) UNet and (ii) text-encoder of the diffusion model.
Text-Guided Vector Graphics Customization
In this paper, we propose a novel pipeline that generates high-quality customized vector graphics based on textual prompts while preserving the properties and layer-wise information of a given exemplar SVG.
NPF-200: A Multi-Modal Eye Fixation Dataset and Method for Non-Photorealistic Videos
Non-photorealistic videos are in demand with the wave of the metaverse, but lack of sufficient research studies.
Language-based Photo Color Adjustment for Graphic Designs
We also introduce an approach for generating a synthetic graphic design dataset with instructions to enable model training.
FashionTex: Controllable Virtual Try-on with Text and Texture
Virtual try-on attracts increasing research attention as a promising way for enhancing the user experience for online cloth shopping.
AssetField: Assets Mining and Reconfiguration in Ground Feature Plane Representation
Traditional modeling pipelines keep an asset library storing unique object templates, which is both versatile and memory efficient in practice.
Grid-guided Neural Radiance Fields for Large Urban Scenes
An alternative solution is to use a feature grid representation, which is computationally efficient and can naturally scale to a large scene with increased grid resolutions.
Neural Preset for Color Style Transfer
In this paper, we present a Neural Preset technique to address the limitations of existing color style transfer methods, including visual artifacts, vast memory requirement, and slow style switching speed.
UniColor: A Unified Framework for Multi-Modal Colorization with Transformer
In the first stage, multi-modal conditions are converted into a common representation of hint points.
TransRank: Self-supervised Video Representation Learning via Ranking-based Transformation Recognition
To mitigate this problem, we developed TransRank, a unified framework for recognizing Transformations in a Ranking formulation.
BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering
The wide span of viewing positions within these scenes yields multi-scale renderings with very different levels of detail, which poses great challenges to neural radiance field and biases it towards compromised results.
Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation
While the average prediction accuracy has been improved significantly over the years, the performance on hard poses with depth ambiguity, self-occlusion, and complex or rare poses is still far from satisfactory.
Ranked #23 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Unifying Global-Local Representations in Salient Object Detection with Transformer
In this paper, we introduce a new attention-based encoder, vision transformer, into salient object detection to ensure the globalization of the representations from shallow to deep layers.
Delving Deep Into Many-to-Many Attention for Few-Shot Video Object Segmentation
The key is to model the relationship between the query videos and the support images for propagating the object information.
BlockPlanner: City Block Generation With Vectorized Graph Representation
City modeling is the foundation for computational urban planning, navigation, and entertainment.
What makes instance discrimination good for transfer learning?
Contrastive visual pretraining based on the instance discrimination pretext task has made significant progress.
Distilling Localization for Self-Supervised Representation Learning
To address this problem, we propose a data-driven approach for learning invariance to backgrounds.
